Préparation des données :

## region_viticole  cepage              
## Alsace           gewurztraminer          231
##                  pinot auxerrois         137
##                  riesling                229
## Bordelais        cabernet franc          118
##                  cabernet sauvignon      522
##                  merlot                  278
##                  sauvignon blanc         257
##                  semillon                139
## Bourgogne        chardonnay              183
##                  gamay                   117
##                  meunier                   3
##                  pinot noir              203
## Champagne        chardonnay              403
##                  meunier                 354
##                  pinot noir                6
## Charentes        cabernet sauvignon        3
##                  ugni blanc              287
## Cotes-du-Rhone   cinsault                 12
##                  gamay                    11
##                  grenache                 23
##                  muscat de hambourg       22
##                  muscat petits grains      6
##                  syrah                    26
## Jura             poulsard                 87
##                  savagnin                207
##                  trousseau               213
## Languedoc        cabernet franc            8
##                  cabernet sauvignon       17
##                  carignan                  8
##                  chardonnay               17
##                  mourvedre                 4
##                  muscat petits grains     19
##                  pinot noir               14
##                  sauvignon blanc           6
## Provence         cabernet sauvignon       98
##                  carignan                 21
##                  cinsault                 24
##                  grenache                 34
##                  mourvedre                22
##                  muscat de hambourg       36
##                  syrah                    27
## Val de Loire     cabernet franc          178
##                  chenin                  319
##                  melon                   275
##                  sauvignon blanc         285
## dtype: int64

Résumé des incidences par régions :

##                 pourcentage_esca                    
##                            count      mean       std
## region_viticole                                     
## Alsace                     597.0  5.659014  5.796469
## Bordelais                 1314.0  3.966113  5.312952
## Bourgogne                  506.0  3.015018  4.670658
## Champagne                  763.0  0.783341  1.016968
## Charentes                  290.0  7.322215  6.424814
## Cotes-du-Rhone             100.0  1.930000  3.586449
## Jura                       507.0  9.424852  8.850297
## Languedoc                   93.0  5.326325  9.302493
## Provence                   262.0  4.170483  5.762116
## Val de Loire              1057.0  6.453154  5.976168
## count    5489.000000
## mean        4.776286
## std         6.154520
## min         0.000000
## 25%         0.666667
## 50%         2.483248
## 75%         6.666667
## max        63.333333
## Name: pourcentage_esca, dtype: float64

XGBoost régionaux généraux :

XGBRegressor(base_score=None, booster=None, callbacks=None,
             colsample_bylevel=None, colsample_bynode=None,
             colsample_bytree=None, device=None, early_stopping_rounds=None,
             enable_categorical=False, eval_metric=None, feature_types=None,
             feature_weights=None, gamma=None, grow_policy=None,
             importance_type=None, interaction_constraints=None,
             learning_rate=0.1, max_bin=None, max_cat_threshold=None,
             max_cat_to_onehot=None, max_delta_step=None, max_depth=6,
             max_leaves=None, min_child_weight=None, missing=nan,
             monotone_constraints=None, multi_strategy=None, n_estimators=100,
             n_jobs=None, num_parallel_tree=None, ...)
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##            region       rmse       mae
## 0          Alsace   3.928968  2.794687
## 1       Bordelais   4.490610  2.825330
## 2       Bourgogne   3.945469  2.404211
## 3       Champagne   1.152085  0.728599
## 4       Charentes   8.204814  6.017232
## 5  Cotes-du-Rhone   5.512553  2.837171
## 6            Jura   6.102994  4.427816
## 7       Languedoc  11.606178  5.073838
## 8        Provence   6.012571  3.526684
## 9    Val de Loire   6.185696  4.360010

Alsace

Cross-validation :

Création du meilleur modèle :

XGBRegressor(base_score=None, booster=None, callbacks=None,
             colsample_bylevel=None, colsample_bynode=None,
             colsample_bytree=None, device=None, early_stopping_rounds=None,
             enable_categorical=False, eval_metric=None, feature_types=None,
             feature_weights=None, gamma=None, grow_policy=None,
             importance_type=None, interaction_constraints=None,
             learning_rate=0.05, max_bin=None, max_cat_threshold=None,
             max_cat_to_onehot=None, max_delta_step=None, max_depth=3,
             max_leaves=None, min_child_weight=None, missing=nan,
             monotone_constraints=None, multi_strategy=None, n_estimators=200,
             n_jobs=None, num_parallel_tree=None, ...)
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## 
## Train RMSE:
## 3.0787242981220952
## 
## Train mean error:
## 2.256282784979964
## 
## Train r²:
## 0.7559271543002186
## 
## Test RMSE:
## 5.789010178963023
## 
## Test mean error:
## 3.603371055598425
## 
## Test r²:
## 0.16419893386793669

Importance des variables :

XGBRegressor(base_score=None, booster=None, callbacks=None,
             colsample_bylevel=None, colsample_bynode=None,
             colsample_bytree=None, device=None, early_stopping_rounds=None,
             enable_categorical=False, eval_metric=None, feature_types=None,
             feature_weights=None, gamma=None, grow_policy=None,
             importance_type=None, interaction_constraints=None,
             learning_rate=0.05, max_bin=None, max_cat_threshold=None,
             max_cat_to_onehot=None, max_delta_step=None, max_depth=3,
             max_leaves=None, min_child_weight=None, missing=nan,
             monotone_constraints=None, multi_strategy=None, n_estimators=200,
             n_jobs=None, num_parallel_tree=None, ...)
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## <IPython.core.display.HTML object>

Bordelais

Cross-validation :

Création du meilleur modèle :

XGBRegressor(base_score=None, booster=None, callbacks=None,
             colsample_bylevel=None, colsample_bynode=None,
             colsample_bytree=None, device=None, early_stopping_rounds=None,
             enable_categorical=False, eval_metric=None, feature_types=None,
             feature_weights=None, gamma=None, grow_policy=None,
             importance_type=None, interaction_constraints=None,
             learning_rate=0.1, max_bin=None, max_cat_threshold=None,
             max_cat_to_onehot=None, max_delta_step=None, max_depth=3,
             max_leaves=None, min_child_weight=None, missing=nan,
             monotone_constraints=None, multi_strategy=None, n_estimators=200,
             n_jobs=None, num_parallel_tree=None, ...)
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## 
## Train RMSE:
## 2.786107886604798
## 
## Train mean error:
## 1.907100895849971
## 
## Train r²:
## 0.7776664141232649
## 
## Test RMSE:
## 3.783499915231343
## 
## Test mean error:
## 2.4633329924762033
## 
## Test r²:
## 0.40421613802953654

Importance des variables :

XGBRegressor(base_score=None, booster=None, callbacks=None,
             colsample_bylevel=None, colsample_bynode=None,
             colsample_bytree=None, device=None, early_stopping_rounds=None,
             enable_categorical=False, eval_metric=None, feature_types=None,
             feature_weights=None, gamma=None, grow_policy=None,
             importance_type=None, interaction_constraints=None,
             learning_rate=0.1, max_bin=None, max_cat_threshold=None,
             max_cat_to_onehot=None, max_delta_step=None, max_depth=3,
             max_leaves=None, min_child_weight=None, missing=nan,
             monotone_constraints=None, multi_strategy=None, n_estimators=200,
             n_jobs=None, num_parallel_tree=None, ...)
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## <IPython.core.display.HTML object>

Bourgogne

Cross-validation :

Création du meilleur modèle :

XGBRegressor(base_score=None, booster=None, callbacks=None,
             colsample_bylevel=None, colsample_bynode=None,
             colsample_bytree=None, device=None, early_stopping_rounds=None,
             enable_categorical=False, eval_metric=None, feature_types=None,
             feature_weights=None, gamma=None, grow_policy=None,
             importance_type=None, interaction_constraints=None,
             learning_rate=0.01, max_bin=None, max_cat_threshold=None,
             max_cat_to_onehot=None, max_delta_step=None, max_depth=6,
             max_leaves=None, min_child_weight=None, missing=nan,
             monotone_constraints=None, multi_strategy=None, n_estimators=200,
             n_jobs=None, num_parallel_tree=None, ...)
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## 
## Train RMSE:
## 3.0973031211558157
## 
## Train mean error:
## 1.7489621122169825
## 
## Train r²:
## 0.8761305168095069
## 
## Test RMSE:
## 2.986497180684029
## 
## Test mean error:
## 2.0043854641345913
## 
## Test r²:
## 0.1756280495866092

Importance des variables :

XGBRegressor(base_score=None, booster=None, callbacks=None,
             colsample_bylevel=None, colsample_bynode=None,
             colsample_bytree=None, device=None, early_stopping_rounds=None,
             enable_categorical=False, eval_metric=None, feature_types=None,
             feature_weights=None, gamma=None, grow_policy=None,
             importance_type=None, interaction_constraints=None,
             learning_rate=0.01, max_bin=None, max_cat_threshold=None,
             max_cat_to_onehot=None, max_delta_step=None, max_depth=6,
             max_leaves=None, min_child_weight=None, missing=nan,
             monotone_constraints=None, multi_strategy=None, n_estimators=200,
             n_jobs=None, num_parallel_tree=None, ...)
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## <IPython.core.display.HTML object>

Champagne

Cross-validation :

Création du meilleur modèle :

XGBRegressor(base_score=None, booster=None, callbacks=None,
             colsample_bylevel=None, colsample_bynode=None,
             colsample_bytree=None, device=None, early_stopping_rounds=None,
             enable_categorical=False, eval_metric=None, feature_types=None,
             feature_weights=None, gamma=None, grow_policy=None,
             importance_type=None, interaction_constraints=None,
             learning_rate=0.01, max_bin=None, max_cat_threshold=None,
             max_cat_to_onehot=None, max_delta_step=None, max_depth=3,
             max_leaves=None, min_child_weight=None, missing=nan,
             monotone_constraints=None, multi_strategy=None, n_estimators=100,
             n_jobs=None, num_parallel_tree=None, ...)
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## 
## Train RMSE:
## 0.9377091871661601
## 
## Train mean error:
## 0.6653347966165406
## 
## Train r²:
## 0.2649591641198136
## 
## Test RMSE:
## 1.0806526118892725
## 
## Test mean error:
## 0.7485246410795099
## 
## Test r²:
## 0.009004906854850642

Importance des variables :

XGBRegressor(base_score=None, booster=None, callbacks=None,
             colsample_bylevel=None, colsample_bynode=None,
             colsample_bytree=None, device=None, early_stopping_rounds=None,
             enable_categorical=False, eval_metric=None, feature_types=None,
             feature_weights=None, gamma=None, grow_policy=None,
             importance_type=None, interaction_constraints=None,
             learning_rate=0.01, max_bin=None, max_cat_threshold=None,
             max_cat_to_onehot=None, max_delta_step=None, max_depth=3,
             max_leaves=None, min_child_weight=None, missing=nan,
             monotone_constraints=None, multi_strategy=None, n_estimators=100,
             n_jobs=None, num_parallel_tree=None, ...)
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## <IPython.core.display.HTML object>

Charentes

Cross-validation :

Création du meilleur modèle :

XGBRegressor(base_score=None, booster=None, callbacks=None,
             colsample_bylevel=None, colsample_bynode=None,
             colsample_bytree=None, device=None, early_stopping_rounds=None,
             enable_categorical=False, eval_metric=None, feature_types=None,
             feature_weights=None, gamma=None, grow_policy=None,
             importance_type=None, interaction_constraints=None,
             learning_rate=0.1, max_bin=None, max_cat_threshold=None,
             max_cat_to_onehot=None, max_delta_step=None, max_depth=3,
             max_leaves=None, min_child_weight=None, missing=nan,
             monotone_constraints=None, multi_strategy=None, n_estimators=50,
             n_jobs=None, num_parallel_tree=None, ...)
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## 
## Train RMSE:
## 4.208786091751969
## 
## Train mean error:
## 3.2356591253332585
## 
## Train r²:
## 0.6874715793415148
## 
## Test RMSE:
## 5.175055672311247
## 
## Test mean error:
## 4.19543047717014
## 
## Test r²:
## 0.27600028608446914

Importance des variables :

XGBRegressor(base_score=None, booster=None, callbacks=None,
             colsample_bylevel=None, colsample_bynode=None,
             colsample_bytree=None, device=None, early_stopping_rounds=None,
             enable_categorical=False, eval_metric=None, feature_types=None,
             feature_weights=None, gamma=None, grow_policy=None,
             importance_type=None, interaction_constraints=None,
             learning_rate=0.1, max_bin=None, max_cat_threshold=None,
             max_cat_to_onehot=None, max_delta_step=None, max_depth=3,
             max_leaves=None, min_child_weight=None, missing=nan,
             monotone_constraints=None, multi_strategy=None, n_estimators=50,
             n_jobs=None, num_parallel_tree=None, ...)
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## <IPython.core.display.HTML object>

Cotes-du-Rhone

Cross-validation :

Création du meilleur modèle :

XGBRegressor(base_score=None, booster=None, callbacks=None,
             colsample_bylevel=None, colsample_bynode=None,
             colsample_bytree=None, device=None, early_stopping_rounds=None,
             enable_categorical=False, eval_metric=None, feature_types=None,
             feature_weights=None, gamma=None, grow_policy=None,
             importance_type=None, interaction_constraints=None,
             learning_rate=0.1, max_bin=None, max_cat_threshold=None,
             max_cat_to_onehot=None, max_delta_step=None, max_depth=3,
             max_leaves=None, min_child_weight=None, missing=nan,
             monotone_constraints=None, multi_strategy=None, n_estimators=50,
             n_jobs=None, num_parallel_tree=None, ...)
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## 
## Train RMSE:
## 1.2738705832904655
## 
## Train mean error:
## 0.8710663345952826
## 
## Train r²:
## 0.9321605484956036
## 
## Test RMSE:
## 2.7642431801188363
## 
## Test mean error:
## 1.530050558348497
## 
## Test r²:
## 0.34859857054930976

Importance des variables :

XGBRegressor(base_score=None, booster=None, callbacks=None,
             colsample_bylevel=None, colsample_bynode=None,
             colsample_bytree=None, device=None, early_stopping_rounds=None,
             enable_categorical=False, eval_metric=None, feature_types=None,
             feature_weights=None, gamma=None, grow_policy=None,
             importance_type=None, interaction_constraints=None,
             learning_rate=0.1, max_bin=None, max_cat_threshold=None,
             max_cat_to_onehot=None, max_delta_step=None, max_depth=3,
             max_leaves=None, min_child_weight=None, missing=nan,
             monotone_constraints=None, multi_strategy=None, n_estimators=50,
             n_jobs=None, num_parallel_tree=None, ...)
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## <IPython.core.display.HTML object>

Jura

Cross-validation :

Création du meilleur modèle :

XGBRegressor(base_score=None, booster=None, callbacks=None,
             colsample_bylevel=None, colsample_bynode=None,
             colsample_bytree=None, device=None, early_stopping_rounds=None,
             enable_categorical=False, eval_metric=None, feature_types=None,
             feature_weights=None, gamma=None, grow_policy=None,
             importance_type=None, interaction_constraints=None,
             learning_rate=0.05, max_bin=None, max_cat_threshold=None,
             max_cat_to_onehot=None, max_delta_step=None, max_depth=3,
             max_leaves=None, min_child_weight=None, missing=nan,
             monotone_constraints=None, multi_strategy=None, n_estimators=200,
             n_jobs=None, num_parallel_tree=None, ...)
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## 
## Train RMSE:
## 4.769345475468805
## 
## Train mean error:
## 3.470189957225011
## 
## Train r²:
## 0.7668068118030442
## 
## Test RMSE:
## 6.1486722787250905
## 
## Test mean error:
## 4.311950152772929
## 
## Test r²:
## 0.3680538507805982

Importance des variables :

XGBRegressor(base_score=None, booster=None, callbacks=None,
             colsample_bylevel=None, colsample_bynode=None,
             colsample_bytree=None, device=None, early_stopping_rounds=None,
             enable_categorical=False, eval_metric=None, feature_types=None,
             feature_weights=None, gamma=None, grow_policy=None,
             importance_type=None, interaction_constraints=None,
             learning_rate=0.05, max_bin=None, max_cat_threshold=None,
             max_cat_to_onehot=None, max_delta_step=None, max_depth=3,
             max_leaves=None, min_child_weight=None, missing=nan,
             monotone_constraints=None, multi_strategy=None, n_estimators=200,
             n_jobs=None, num_parallel_tree=None, ...)
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## <IPython.core.display.HTML object>

Languedoc

Cross-validation :

Création du meilleur modèle :

XGBRegressor(base_score=None, booster=None, callbacks=None,
             colsample_bylevel=None, colsample_bynode=None,
             colsample_bytree=None, device=None, early_stopping_rounds=None,
             enable_categorical=False, eval_metric=None, feature_types=None,
             feature_weights=None, gamma=None, grow_policy=None,
             importance_type=None, interaction_constraints=None,
             learning_rate=0.1, max_bin=None, max_cat_threshold=None,
             max_cat_to_onehot=None, max_delta_step=None, max_depth=3,
             max_leaves=None, min_child_weight=None, missing=nan,
             monotone_constraints=None, multi_strategy=None, n_estimators=200,
             n_jobs=None, num_parallel_tree=None, ...)
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## 
## Train RMSE:
## 0.3777447263639509
## 
## Train mean error:
## 0.27238823997420886
## 
## Train r²:
## 0.9982656624303965
## 
## Test RMSE:
## 10.861559162482653
## 
## Test mean error:
## 5.368725670661857
## 
## Test r²:
## 0.1631265783730237

Importance des variables :

XGBRegressor(base_score=None, booster=None, callbacks=None,
             colsample_bylevel=None, colsample_bynode=None,
             colsample_bytree=None, device=None, early_stopping_rounds=None,
             enable_categorical=False, eval_metric=None, feature_types=None,
             feature_weights=None, gamma=None, grow_policy=None,
             importance_type=None, interaction_constraints=None,
             learning_rate=0.1, max_bin=None, max_cat_threshold=None,
             max_cat_to_onehot=None, max_delta_step=None, max_depth=3,
             max_leaves=None, min_child_weight=None, missing=nan,
             monotone_constraints=None, multi_strategy=None, n_estimators=200,
             n_jobs=None, num_parallel_tree=None, ...)
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## <IPython.core.display.HTML object>

Provence

Cross-validation :

Création du meilleur modèle :

XGBRegressor(base_score=None, booster=None, callbacks=None,
             colsample_bylevel=None, colsample_bynode=None,
             colsample_bytree=None, device=None, early_stopping_rounds=None,
             enable_categorical=False, eval_metric=None, feature_types=None,
             feature_weights=None, gamma=None, grow_policy=None,
             importance_type=None, interaction_constraints=None,
             learning_rate=0.01, max_bin=None, max_cat_threshold=None,
             max_cat_to_onehot=None, max_delta_step=None, max_depth=3,
             max_leaves=None, min_child_weight=None, missing=nan,
             monotone_constraints=None, multi_strategy=None, n_estimators=200,
             n_jobs=None, num_parallel_tree=None, ...)
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## 
## Train RMSE:
## 4.415485518203939
## 
## Train mean error:
## 2.8600886807297212
## 
## Train r²:
## 0.6399318912231008
## 
## Test RMSE:
## 3.2303132434780206
## 
## Test mean error:
## 2.59528615939542
## 
## Test r²:
## 0.3872593632016734

Importance des variables :

XGBRegressor(base_score=None, booster=None, callbacks=None,
             colsample_bylevel=None, colsample_bynode=None,
             colsample_bytree=None, device=None, early_stopping_rounds=None,
             enable_categorical=False, eval_metric=None, feature_types=None,
             feature_weights=None, gamma=None, grow_policy=None,
             importance_type=None, interaction_constraints=None,
             learning_rate=0.01, max_bin=None, max_cat_threshold=None,
             max_cat_to_onehot=None, max_delta_step=None, max_depth=3,
             max_leaves=None, min_child_weight=None, missing=nan,
             monotone_constraints=None, multi_strategy=None, n_estimators=200,
             n_jobs=None, num_parallel_tree=None, ...)
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## <IPython.core.display.HTML object>

Val de Loire

Cross-validation :

Création du meilleur modèle :

XGBRegressor(base_score=None, booster=None, callbacks=None,
             colsample_bylevel=None, colsample_bynode=None,
             colsample_bytree=None, device=None, early_stopping_rounds=None,
             enable_categorical=False, eval_metric=None, feature_types=None,
             feature_weights=None, gamma=None, grow_policy=None,
             importance_type=None, interaction_constraints=None,
             learning_rate=0.1, max_bin=None, max_cat_threshold=None,
             max_cat_to_onehot=None, max_delta_step=None, max_depth=3,
             max_leaves=None, min_child_weight=None, missing=nan,
             monotone_constraints=None, multi_strategy=None, n_estimators=50,
             n_jobs=None, num_parallel_tree=None, ...)
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## 
## Train RMSE:
## 4.88243531346175
## 
## Train mean error:
## 3.705909269500348
## 
## Train r²:
## 0.4626109856818992
## 
## Test RMSE:
## 5.034684531444679
## 
## Test mean error:
## 3.74259909414069
## 
## Test r²:
## 0.11641425421063563

Importance des variables :

XGBRegressor(base_score=None, booster=None, callbacks=None,
             colsample_bylevel=None, colsample_bynode=None,
             colsample_bytree=None, device=None, early_stopping_rounds=None,
             enable_categorical=False, eval_metric=None, feature_types=None,
             feature_weights=None, gamma=None, grow_policy=None,
             importance_type=None, interaction_constraints=None,
             learning_rate=0.1, max_bin=None, max_cat_threshold=None,
             max_cat_to_onehot=None, max_delta_step=None, max_depth=3,
             max_leaves=None, min_child_weight=None, missing=nan,
             monotone_constraints=None, multi_strategy=None, n_estimators=50,
             n_jobs=None, num_parallel_tree=None, ...)
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## <IPython.core.display.HTML object>